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RGB-D Mapping:Using Depth Cameras for Dense 3D Modeling of

Indoor Environments

Peter Henry1, Michael Krainin1, Evan Herbst1,Xiaofeng Ren2, and Dieter Fox1,2

1University of Washington Computer Science and Engineering2Intel Labs Seattle

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RGB-D Data

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Related Work SLAM

[Davison et al, PAMI 2007] (monocular) [Konolige et al, IJR 2010] (stereo) [Pollefeys et al, IJCV 2007] (multi-view stereo) [Borrmann et al, RAS 2008] (3D laser) [May et al, JFR 2009] (ToF sensor)

Loop Closure Detection [Nister et al, CVPR 2006] [Paul et al, ICRA 2010]

Photo collections [Snavely et al, SIGGRAPH 2006] [Furukawa et al, ICCV 2009]

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System Overview

1. Frame-to-frame alignment

2. Loop closure detection and global consistency

3. Map representation

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Alignment (3D SIFT RANSAC)

SIFT features (from image) in 3D (from depth)

RANSAC alignment requires no initial estimate

Requires sufficient matched features

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RANSAC Failure Case

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Alignment (ICP)

Does not need visual features or color

Requires sufficient geometry and initial estimate

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ICP Failure Case

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Joint Optimization (RGBD-ICP)

Fixed feature matches Dense point-to-plane

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Loop ClosureSequential alignments accumulate error

Revisiting a previous location results in an inconsistent map

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Loop Closure DetectionDetect by running RANSAC against previous

frames

Pre-filter options (for efficiency):Only a subset of frames (keyframes)

Every nth frameNew Keyframe when RANSAC fails directly against

previous keyframeOnly keyframes with similar estimated 3D posePlace recognition using vocabulary tree

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Loop Closure CorrectionTORO [Grisetti 2007]:

Constraints between camera locations in pose graph

Maximum likelihood global camera poses

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Map Representation: Surfels

Surface Elements [Pfister 2000, Weise 2009, Krainin 2010]

Circular surface patches

Accumulate color / orientation / size information

Incremental, independent updates

Incorporate occlusion reasoning

750 million points reduced to 9 million surfels

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Experiment 1: RGBD-ICP16 locations marked in environment (~7m

apart)

Camera on tripod carried between and placed at markers

Error between ground truth distance and the accumulated distance from frame-to-frame alignment

α weight 0.0 (ICP) 0.2 0.5 0.8 1.0 (RANSAC)

Mean error (m)

0.22 0.19

0.16

0.18

1.29

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Experiment 2: Robot ArmCamera held in WAM arm for ground truth pose

Method

Mean Translation

al Error (m)

Mean Angular

Error (deg)

RANSAC .168 4.5

ICP .144 3.9

RGBD-ICP (α=0.5) .123 3.3

RGBD-ICP + TORO

.069 2.8

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Map Accuracy: Architectural Drawing

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Map Accuracy: 2D Laser Map

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ConclusionKinect-style depth cameras have recently

become available as consumer products

RGB-D Mapping can generate rich 3D maps using these cameras

RGBD-ICP combines visual and shape information for robust frame-to-frame alignment

Global consistency achieved via loop closure detection and optimization (RANSAC, TORO, SBA)

Surfels provide a compact map representation

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Open QuestionsWhich are the best features to use?

How to find more loop closure constraints between frames?

What is the right representation (point clouds, surfels, meshes, volumetric, geometric primitives, objects)?

How to generate increasingly photorealistic maps?

Autonomous exploration for map completeness?

Can we use these rich 3D maps for semantic mapping?

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Thank You!